Effective Social Circle Prediction based on Bayesian Network

User's personal social networks are big and cluttered, yet contain highly valuable information. Organizing users' friends into circles or communities is a fundamental task in social network research. Social network sites allow users to manually categorize their friends into social circles; however this process is laborious and inadaptable to changes. In this paper, we study novel ways of automatically determining users' social circles. We treat this task as a classification problem on a user's ego-network, a network of connections between friends. Based on Bayesian Network (BN), we develop a model for determining whether a query user U-q is in main user U-m's social circle. First, we transform the original social network data to make it suitable for BN modeling, and build an Initial Bayesian Network (IBN) of U-m using the state-of-the-art BN learning algorithm. Then, we propose a new method to improve the IBN by adding important parents to the class variable. Lastly, leveraging carefully designed threshold, we use the final BN to determine the existence of U-q in the social circle of U-m. Modeling social circle with BN allows us to quantify user's social circle existence with probability and run query with missing values/evidences. Using ground-truth data from Facebook and Twitter, experimental results indicate that our BN model could accurately determine user's existence in social circle and outperforms four baseline predictors, namely Naive Bayes, IBL, OneR and J48, showing promising application potential in the social circle research area.